• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1462-1471
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• 2005

Recently, the medical image has been digitized by the development of computer science and digitization of the medical devices. There are needs for database service of the medical image and long term storage because of the construction of PACS(picture archiving and communication system) following DICOM(digital imaging communications in medicine) standards, telemedicine, and et al. However, it also caused some kinds of problems, such as illegal reproduction of medical image, proprietary rights and data authentication. In this paper, we propose the new digital watermarking technique for medical image based on public key encryption algorithm for integrity verification. It prevents illegal forgery that can be caused after transmitting medical image data remotely. The watermark is the value of bit-plane in wavelet transform of the original image for certification method of integrity verification. We proposed the embedding regions are randomly chosen considering ROI, and a digital signature is made using hash function of MD5 which input is a secret key. The experimental results show that the watermark embedded by the proposed algorithm can survive successfully in image processing operations and that the watermark's invisibility is good.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.211-216
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• 2018

In this paper, we studied the way that classify whether unknown PE file is malware or not. In the classification problem of malware detection domain, feature extraction and classifier are important. For that purpose, we studied what the feature is good for classifier and the which classifier is good for the selected feature. So, we try to find the good combination of feature and classifier for detecting malware. For it, we did experiments at two step. In step one, we compared the accuracy of features using Opcode only, Win. API only, the one with both. We founded that the feature, Opcode and Win. API, is better than others. In step two, we compared AUC value of classifiers, Bernoulli Naïve Bayes, K-nearest neighbor, Support Vector Machine and Decision Tree. We founded that Decision Tree is better than others.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.420-420
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• 2014

최근, 과학 기술이 발달함에 따라 현장에서의 실시간 검사 및 자가 지단 등 질병 치유에 대한 사람들의 관심이 증가하고 있으며, 이에 따라 의료, 환경, 산업과 같은 많은 분야에서 바이오 센서에 대한 연구가 활발하게 이루어지고 있다. 그 중, EGFET는 전해질 속의 각종 이온 농도를 전기적으로 측정하는 바이오 센서로, 외부 환경으로부터 안전하고, 제작이 쉬우며, 재활용이 가능하여 비용을 절감 할 수 있다는 장점을 가지고 있다 [1]. EGFET는 감지부와 FET부로 분리된 구조를 가지고 있으며, 감지부의 감지막으로는 Al2O3, HfO2, $TiO_2$, SnO2 와 같은 다양한 물질들이 사용되고 있다. 그 중, SnO2는 우수한 감도와 안정성을 가지고 있는 물질로 추가적인 열처리 공정 없이도 우수한 감지 특성을 나타내기 때문에 본 연구에서 감지막으로 사용하였다. 한편, EGFETs 의 FET부로는 기존의 비정질 실리콘 TFTs 에 비해 10배 이상의 높은 이동도와 온/오프 전류비를 갖는 InGaZnO 를 채널층으로 사용한 TFTs 를 사용하였다. a-IGZO 는 넓은 밴드 갭으로 인해 가시광 영역에서 투명하며, 향후 투명 바이오센서 제작 시, 물질들 사이의 반응을 전기적 신호뿐만 아니라 광학적인 분석 방법으로도 검출이 가능하기에 고 신뢰성을 갖는 센서의 제작이 가능할 것으로 기대된다. 한편, a-IGZO TFTs 의 경우 우수한 전기적 특성을 나타냄에도 불구하고 소자 동작 시 문턱 전압이 불안정하다는 단점이 있으며 [2], 이러한 문제의 개선과 향후 투명 기판 위에서의 소자 제작을 위해서는 저온 열처리 공정이 필수적이다. 따라서, 본 연구에서는 저온 열처리 공정인 u-wave 열처리를 통하여 a-IGZO TFTs 의 전기적 특성 및 안정성을 향상시켰으며, 9.51 [$cm2/V{\cdot}s$]의 이동도와 135 [mV/dec] 의 SS값, 0.99 [V]의 문턱 전압, 1.18E+08의 온/오프 전류 비를 갖는 고성능 스위칭 TFTs 를 제작하였다. 최종적으로, 제작된 a-IGZO TFTs 를 SnO2 감지막을 갖는 EGFETs 에 적용함으로써 우수한 감지 특성과 안정성을 갖는 바이오 센서를 제작하였다.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.84-92
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• 2011

The automated segmentation technique of cell region in Bio Images helps biologists understand complex functions of cells. It is mightly important in that it can process the analysis of cells automatically which has been done manually before. The conventional methods for segmentation of cell and nuclei from multi-channel images consist of two steps. In the first step nuclei are extracted from DNA channel, and used as initial contour for the second step. In the second step cytoplasm are segmented from Actin channel by using Active Contour model based on intensity. However, conventional studies have some limitation that they let the cell segmentation performance fall by not considering inhomogeneous intensity problem in cell images. Therefore, the paper consider correlation between DNA and Actin channel, and then proposes the preprocessing algorithm by which the brightness of cell inside in Actin channel can be compensated homogeneously by using DNA channel information. Experiment result show that the proposed preprocessing method improves the cell segmentation performance compared to the conventional method.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.71-77
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• 2014

In this paper, we propose an improved numeric character recognition method which can recognize numeric characters well under low-illuminated and shade-illuminated environment. The LN(Local Normalization) preprocessing method is used in order to enhance low-illuminated and shade-illuminated image quality. The reading area is detected using line segment information extracted from the illumination-normalized meter images, and then the three-phase procedures are performed for segmentation of numeric characters in the reading area. Finally, an efficient hybrid classifier is used to classify the segmented numeric characters. The proposed numeric character classifier is a combination of multi-layered feedforward neural network and template matching module. Robust heuristic rules are applied to classify the numeric characters. Experiments using meter image database were conducted. Meter image database was made using various kinds of meters under low-illuminated and shade-illuminated environment. The experimental results indicates the superiority of the proposed numeric character recognition method.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.3
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• pp.91-96
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• 2014

For Sound source direction and separation method, Frequency Domain Binaural Model(FDBM) shows low computational cost and high performance for sound source separation. This method performs sound source orientation and separation by obtaining the Interaural Phase Difference(IPD) and Interaural Level Difference(ILD) in frequency domain. But the problem of reflection occurs in practical environment. To reduce this reflection, a method to simulate the sound localization of a direct sound, to detect the initial arriving sound, to check the direction of the sound, and to separate the sound is presented. Simulation results show that the direction is estimated to lie close within 10% from the sound source and, in the presence of the reflection, the level of the separation of the sound source is improved by higher Coherence and PESQ(Perceptual Evaluation of Speech Quality) and by lower directional damping than those of the existing FDBM. In case of no reflection, the degree of separation was low.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.209.1-209.1
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• 2013

GaP는 가시광선 발광다이오드을 얻을 수 있는 적절한 재료중의 하나로 해당영역의 파장에 대하여 높은 양자효율을 얻을 수 있고, 깊은 준위 재결합이 없기 때문에 GaP 녹색 및 As 첨가한 GaAsP 적색 LED 에 적용할 수 있습니다. 또한, 상온에서 2.2 eV 에 해당하는 넓은 에너지 밴드갭을 가지고 있으므로, 소음이 없는 자외선 검출기에도 적합합니다. 이 물질에 대한 소자들은 기존에 GaP 기판을 사용하였습니다. 최근, GaP 와 격자상수가 비슷한 Si 기판을 활용하여 그 위에 성장하는 방법에 대한 관심이 많아졌습니다. Si는 물리적 및 화학적으로 안정하고 딱딱한 소재이며 대면적 기판을 쉽게 얻을 수 있어 전자 기기 및 대규모 집적 회로의 좋은 소재입니다. Si 와 대조적으로 GaP은 깨지기 쉬운 재료이며 GaP 기판은 Si와 같은 대면적 기판을 얻을 수 없습니다. 이러한 문제의 한 가지 해결책은 Si 기판위에 GaP 층의 성장입니다. GaP 과 Si의 조합은 현재의 광전소자 들에 더하여 더 많은 응용프로그램들을 가능하게 할 것입니다. 그러나, Si 기판위에 GaP 성장 시 삼차원적 성장 및 역위상 경계면과 같은 문제점들이 발생하므로 질이 높고 균일한 결정의 GaP 를 얻기가 어렵습니다. 따라서, Si 에 GaP 의 성장시 초기 단계를 제어하는 성장 기술이 필요합니다. 본 연구에서는, 유기금속화학증착법을 이용하여 Si 기판위에 양질의 GaP를 얻을 수 있는 최적의 성장조건을 얻고자 합니다. 실험 조건은 Si에 GaP의 에피택셜 성장의 초기 단계에 영향을 주는 V/III 비율, 성장압력, 기판방향 등을 가변하는 조건으로 진행하였습니다. V/III 비율은 100~6400, 성장 압력은 76~380 Torr로 진행하였고, Si 기판은 just(001)과 2~6도 기울어진 (001) 기판을 사용하였습니다.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.23-30
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• 2020

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.15-29
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• 2018

This paper proposes an enhanced object tracking algorithm to compensate the lack of temporal information in existing particle swarm optimization based object trackers using the trajectory of the target object. The proposed scheme also enables the tracking and documentation of the location of an online updated set of distractions. Based on the trajectories information and the distraction set, a rule based approach with adaptive parameters is utilized for occlusion detection and determination of the target position. Compare to existing algorithms, the proposed approach provides more comprehensive use of available information and does not require manual adjustment of threshold values. Moreover, an effective weight adjustment function is proposed to alleviate the diversity loss and pre-mature convergence problem in particle swarm optimization. The proposed weight function ensures particles to search thoroughly in the frame before convergence to an optimum solution. In the existence of multiple objects with similar feature composition, this algorithm is tested to significantly reduce convergence to nearby distractions compared to the other existing swarm intelligence based object trackers.